library(sf)
library(readxl)
library(dplyr)
library(plyr)
library(ggplot2)
library(afrihealthsites)
library(ggpubr)
library(afriadmin)
library(tmap)
library(cowplot)
# Install Malawi MFL
malawi_MFL = read_excel("~/malawi-health-facilities-1/MHFR_Facilities 1.xlsx")
# Convert to sf
## omit NA's
new_malawi_MFL = na.omit(malawi_MFL)
## check for NA
any(is.na(new_malawi_MFL))
## [1] FALSE
## transform geometry columns into numeric
sapply(new_malawi_MFL, class)
## CODE NAME COMMON NAME OWNERSHIP TYPE STATUS
## "character" "character" "character" "character" "character" "character"
## ZONE DISTRICT DATE OPENED LATITUDE LONGITUDE
## "character" "character" "character" "character" "character"
new_malawi_MFL = transform(new_malawi_MFL, LATITUDE = as.numeric(LATITUDE),
LONGITUDE = as.numeric(LONGITUDE))
## Warning in eval(substitute(list(...)), `_data`, parent.frame()): NAs introduced
## by coercion
any(is.na(new_malawi_MFL)) ## check for NA
## [1] TRUE
new_malawi_MFL = na.omit(new_malawi_MFL) ## and omit
## convert to sf object
malawi_facilities_MFL = st_as_sf(new_malawi_MFL, coords = c("LONGITUDE", "LATITUDE"), dim = "XY")
malawi_facilities_MFL = st_set_crs(malawi_facilities_MFL, 4326) ## set CRS, is WGS84 right?
head(malawi_facilities_MFL)
## Simple feature collection with 6 features and 9 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 33.74129 ymin: -15.84 xmax: 35.09 ymax: -13.79742
## geographic CRS: WGS 84
## CODE NAME COMMON.NAME
## 1 MC010002 A + A private clinic A+A
## 2 BT240003 A-C Opticals A.C Opticals
## 3 BT240005 Akwezeke PVT Clinic Akwezeke Pvt
## 4 BT240006 AB Medical Clinic Abowa
## 5 LL040007 ABC Comm. Hospital ABC Clinic
## 6 LL040010 Achikondi Women Community Friendly Services Clinic Achikondi
## OWNERSHIP TYPE STATUS
## 1 Private Clinic Functional
## 2 Private Clinic Functional
## 3 Private Clinic Functional
## 4 Private Clinic Functional
## 5 Christian Health Association of Malawi (CHAM) Hospital Functional
## 6 Private Dispensary Functional
## ZONE DISTRICT DATE.OPENED geometry
## 1 Centrals West Zone Mchinji Jan 1st 75 POINT (33.88563 -13.79742)
## 2 South East Zone Blantyre Jan 1st 75 POINT (35.03 -15.8)
## 3 South East Zone Blantyre Jan 1st 75 POINT (35.09 -15.84)
## 4 South East Zone Blantyre Jan 1st 75 POINT (35.09 -15.84)
## 5 Centrals West Zone Lilongwe Jan 1st 75 POINT (33.74129 -13.96816)
## 6 Centrals West Zone Lilongwe Jan 1st 75 POINT (33.7793 -13.95473)
Overview:
# Re-order the facility types
malawi_MFL$TYPE = as.factor(malawi_MFL$TYPE)
malawi_MFL$TYPE = factor(malawi_MFL$TYPE, levels = c("Central Hospital", "District Hospital", "Hospital", "Health Centre", "Clinic", "Health Post", "Dispensary", "Private", "Unclassified"))
# Number of each type + ownership
facility_types_MFL = as.data.frame(table(malawi_MFL$TYPE, malawi_MFL$OWNERSHIP))
head(facility_types_MFL)
## Var1 Var2 Freq
## 1 Central Hospital Aquaid Lifeline 0
## 2 District Hospital Aquaid Lifeline 0
## 3 Hospital Aquaid Lifeline 0
## 4 Health Centre Aquaid Lifeline 0
## 5 Clinic Aquaid Lifeline 0
## 6 Health Post Aquaid Lifeline 0
## bar plot of no. of facility types
plot_facility_types_MFL = ggplot(facility_types_MFL, aes(x=Var1, y=Freq, fill=Var2)) + geom_bar(position = "stack", stat = "identity")
plot_facility_types_MFL = plot_facility_types_MFL + labs(x = "Facility types", y = "Frequency", fill="Ownership") + scale_fill_brewer(palette = "Set2") + coord_flip() + theme_pubclean() + ggtitle("MFL") + theme(legend.title = element_text(size = 9), legend.text = element_text(size = 9), plot.title = element_text(face = "bold", hjust = 0), legend.position = "bottom")
plot_facility_types_MFL
par(mar=c(11,4,4,4))
# Re-order ownership
malawi_MFL$OWNERSHIP = as.factor(malawi_MFL$OWNERSHIP)
malawi_MFL$OWNERSHIP = factor(malawi_MFL$OWNERSHIP, levels = c("Government", "Private", "Christian Health Association of Malawi (CHAM)", "Non-Government", "Mission/Faith-based (other than CHAM)", "Other", "Parastatal", "Aquaid Lifeline"))
# Number of each type of ownership
ownership_MFL = as.data.frame(table(malawi_MFL$OWNERSHIP))
ownership_MFL
## Var1 Freq
## 1 Government 695
## 2 Private 495
## 3 Christian Health Association of Malawi (CHAM) 192
## 4 Non-Government 69
## 5 Mission/Faith-based (other than CHAM) 62
## 6 Other 27
## 7 Parastatal 5
## 8 Aquaid Lifeline 1
## bar plot of ownership
plot_ownership_MFL = ggplot(ownership_MFL, aes(x=Var1, y=Freq)) + geom_bar(stat = "identity", fill="slategray")
plot_ownership_MFL = plot_ownership_MFL + labs(y = "Frequency") + coord_flip() + theme_pubclean() + theme(axis.title.y = element_blank()) + ggtitle("Ownership")
plot_ownership_MFL
Focuses on facilities run by government, faith-based organisations, NGO’s and local authorities. Covers 50 countries in sub-Saharan Africa. Sources of information include health sector reports, websites run by national or international organisations and personal communications
If MFL was available it was used. More than one datasource was often used to compile facility list
Private facilities are excluded, duplicates removed, name errors corrected and name variations were matched. Missing info was added with the use of other datasources.
Now hosted by the WHO Global Malaria Programme, last update February 2019
Malawi datasources includes MFL, https://data.humdata.org/dataset/malawi-health and http://www.cham.org.mw/uploads/7/3/0/8/73088105/cham_health_facilities_-_1_june_2016.pdf
At time of publishing, 639 facilities with 9 missing coordinates, not been updated since
Data includes facility name, type, ownership, source of location and reclassified facility types
# Malawi WHO data.frame
malawi_WHO <- afrihealthsites("malawi", datasource='who', plot=FALSE, returnclass='dataframe')
head(malawi_WHO)
## # A tibble: 6 x 10
## Country Admin1 `Facility name` `Facility type` Ownership Lat Long
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Malawi Centr… 80 Block Clinic Clinic MoH -12.9 33.4
## 2 Malawi Centr… ABC Community … Clinic FBO -14.0 33.7
## 3 Malawi Centr… Adventist Heal… Health Centre FBO -14.0 33.8
## 4 Malawi Centr… Alinafe Commun… Community Hosp… FBO -13.4 34.2
## 5 Malawi Centr… Area 18 Health… Health Centre MoH -13.9 33.8
## 6 Malawi Centr… Area 25 Health… Health Centre MoH -13.9 33.8
## # … with 3 more variables: `LL source` <chr>, iso3c <chr>,
## # facility_type_9 <chr>
# Re order facility types and ownership
malawi_WHO$`Facility type` = as.factor(malawi_WHO$`Facility type`)
malawi_WHO$`Facility type` = factor(malawi_WHO$`Facility type`, levels = c("Central Hospital", "District Hospital", "Mission Hospital", "Rural Hospital", "Community Hospital", "Health Centre", "Clinic", "Health Post/Dispensary"))
malawi_WHO$Ownership = as.factor(malawi_WHO$Ownership)
malawi_WHO$Ownership = factor(malawi_WHO$Ownership, levels = c("MoH", "FBO", "Local authority", "NGO"))
# No. of original facility types + ownership
facility_types_WHO = as.data.frame(table(malawi_WHO$`Facility type`, malawi_WHO$Ownership))
head(facility_types_WHO)
## Var1 Var2 Freq
## 1 Central Hospital MoH 4
## 2 District Hospital MoH 24
## 3 Mission Hospital MoH 0
## 4 Rural Hospital MoH 17
## 5 Community Hospital MoH 0
## 6 Health Centre MoH 332
## bar plot of original facility types
plot_facility_types_WHO = ggplot(facility_types_WHO, aes(x=Var1, y=Freq, fill=Var2)) + geom_bar(position = "stack", stat = "identity")
plot_facility_types_WHO = plot_facility_types_WHO + labs(x = "Facility types", y = "Frequency", fill="Ownership") + scale_fill_brewer(palette = "Set2") + coord_flip() + theme_pubclean() + ggtitle("WHO") + theme(legend.title = element_text(size = 9), legend.text = element_text(size = 9), plot.title = element_text(face = "bold", hjust = 0), legend.position = "bottom")
plot_facility_types_WHO
# Re order
malawi_WHO$facility_type_9 = as.factor(malawi_WHO$facility_type_9)
malawi_WHO$facility_type_9 = factor(malawi_WHO$facility_type_9, levels = c("Hospital", "Health Centre", "Health Clinic", "Health Post", "Community Health Unit"))
# No. of reclassified facility types
RC_facility_types_WHO = as.data.frame(table(malawi_WHO$facility_type_9))
RC_facility_types_WHO
## Var1 Freq
## 1 Hospital 80
## 2 Health Centre 457
## 3 Health Clinic 22
## 4 Health Post 87
## 5 Community Health Unit 2
## bar plot of reclassified facility types
plot_RC_facility_types_WHO = ggplot(RC_facility_types_WHO, aes(x=Var1, y=Freq)) + geom_bar(stat = "identity", fill="slategray")
plot_RC_facility_types_WHO = plot_RC_facility_types_WHO + labs(y = "Frequency") + coord_flip() + theme_pubclean() + theme(axis.title.y = element_blank()) + ggtitle("Reclassified facility types")
plot_RC_facility_types_WHO
# Types of ownership
ownership_WHO = as.data.frame(table(malawi_WHO$Ownership))
ownership_WHO
## Var1 Freq
## 1 MoH 467
## 2 FBO 173
## 3 Local authority 5
## 4 NGO 3
## bar plot of ownership
plot_ownership_WHO = ggplot(ownership_WHO, aes(x=Var1, y=Freq)) + geom_bar(stat = "identity", fill="slategray")
plot_ownership_WHO = plot_ownership_WHO + labs(y = "Frequency") + coord_flip() + theme_pubclean() + theme(axis.title.y = element_blank()) + ggtitle("Ownership")
plot_ownership_WHO
Both data sources contain no information on services available, capacity or equipment. MFL does state whether facility is functional.
Classification of MFL facilities aligns more with the structure of the health care system in Malawi (community, primary, secondary, tertiary), it differentiates central hospitals from district and other hospitals. WHO has additional rural and mission hospitals, where do they fit in?
https://www.health.gov.mw/index.php/2016-01-06-19-58-23/national-aids states that at community level, health posts, dispensaries and maternity clinics offer services. Primary includes health centers and community hospitals, secondary consists of district and some CHAM hospitals, tertiary includes central hospitals.
Analysis:
## tmap mode set to interactive viewing
## Warning in sf::st_is_longlat(shp2): bounding box has potentially an invalid
## value range for longlat data
## Warning in sf::st_is_longlat(shp2): bounding box has potentially an invalid
## value range for longlat data
## Warning in sf::st_is_longlat(shp2): bounding box has potentially an invalid
## value range for longlat data
Qs to address?:
# Qs 1 - how many intersect?
## convert malawi_WHO to sf object
class(malawi_WHO)
## [1] "tbl_df" "tbl" "data.frame"
any(is.na(malawi_WHO))
## [1] TRUE
new_malawi_WHO = na.omit(malawi_WHO) ## omit NA
sf_malawi_WHO = st_as_sf(new_malawi_WHO, coords = c("Long", "Lat"), dim = "XY")
sf_malawi_WHO = st_set_crs(sf_malawi_WHO, 4326)
## st_intersection
intersect_WHO_MFL = st_intersection(x=sf_malawi_WHO, y=malawi_facilities_MFL)
## although coordinates are longitude/latitude, st_intersection assumes that they are planar
## Warning: attribute variables are assumed to be spatially constant throughout all
## geometries
intersect_WHO_MFL ## only 2 intersect directly, so are same up to 5 decimal places?
## Simple feature collection with 2 features and 17 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 33.29456 ymin: -11.53894 xmax: 33.41925 ymax: -11.45836
## geographic CRS: WGS 84
## # A tibble: 2 x 18
## Country Admin1 Facility.name Facility.type Ownership LL.source iso3c
## * <chr> <chr> <chr> <fct> <fct> <chr> <chr>
## 1 Malawi North… Euthini Heal… Health Centre MoH GPS MWI
## 2 Malawi North… Madede Healt… Health Centre MoH GPS MWI
## # … with 11 more variables: facility_type_9 <fct>, CODE <chr>, NAME <chr>,
## # COMMON.NAME <chr>, OWNERSHIP <chr>, TYPE <fct>, STATUS <chr>, ZONE <chr>,
## # DISTRICT <chr>, DATE.OPENED <chr>, geometry <POINT [°]>
# Qs 2 - Do they share same attributes
## ownership is same, 1 name is same, Euthini registered as a hospital in MFL but as a health centre in WHO
# Qs 3 - how many/which are within 50m of another facility?